mlsanityData to 7 Oct 2026

Lost in Translation: Loss and Decay of Linguistic Richness in Machine Translation

arXiv 1906.12068 ↗First posted 28 Jun 2019cs.CL cs.LGcited by 45

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Abstract (arXiv)

This work presents an empirical approach to quantifying the loss of lexical richness in Machine Translation (MT) systems compared to Human Translation (HT). Our experiments show how current MT systems indeed fail to render the lexical diversity of human generated or translated text. The inability of MT systems to generate diverse outputs and its tendency to exacerbate already frequent patterns while ignoring less frequent ones, might be the underlying cause for, among others, the currently heavily debated issues related to gender biased output. Can we indeed, aside from biased data, talk about an algorithm that exacerbates seen biases?

Key facts. Cited by 45 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Jun 2019.

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Citing papers per year: 2021: 16; 2022: 3; 2023: 1; 2024: 11; 2025: 10; 2026: 4.

45 papers.

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  1. 2021Gender Bias in Machine Translationcited by 116
  2. 2024Bias in Large Language Models: Origin, Evaluation, and Mitigationcited by 77
  3. 2021Machine Translationese: Effects of Algorithmic Bias on Linguistic Complexity in Machine Translationcited by 70
  4. 2024EDT: Improving Large Language Models' Generation by Entropy-based Dynamic Temperature Samplingcited by 42
  5. 2021First the worst: Finding better gender translations during beam searchcited by 28
  6. 2021Investigating Failures of Automatic Translation in the Case of Unambiguous Gendercited by 20
  7. 2022Machine Translation from Signed to Spoken Languages: State of the Art and Challengescited by 20
  8. 2021On the Limits of Minimal Pairs in Contrastive Evaluationcited by 15
  9. 2021How to Split: the Effect of Word Segmentation on Gender Bias in Speech Translationcited by 11
  10. 2021Frequency-Aware Contrastive Learning for Neural Machine Translationcited by 11
  11. 2021The Impact of Post-editing and Machine Translation on Creativity and Reading Experiencecited by 10
  12. 2021Mixup Decoding for Diverse Machine Translationcited by 10
33 more
  1. 2024To Diverge or Not to Diverge: A Morphosyntactic Perspective on Machine Translation vs Human Translationcited by 10
  2. 2023A Survey on Zero Pronoun Translationcited by 9
  3. 2025Are We Paying Attention to Her? Investigating Gender Disambiguation and Attention in Machine Translationcited by 7
  4. 2021Translating the Unseen? Yoruba-English MT in Low-Resource, Morphologically-Unmarked Settingscited by 5
  5. 2021Bilingual Mutual Information Based Adaptive Training for Neural Machine Translationcited by 5
  6. 2022Exploring Diversity in Back Translation for Low-Resource Machine Translationcited by 5
  7. 2024Towards Tailored Recovery of Lexical Diversity in Literary Machine Translationcited by 5
  8. 2025Voice, Bias, and Coreference: An Interpretability Study of Gender in Speech Translationcited by 5
  9. 2021Automatic Classification of Human Translation and Machine Translation: A Study from the Perspective of Lexical Diversitycited by 4
  10. 2021On Systematic Style Differences between Unsupervised and Supervised MT and an Application for High-Resource Machine Translationcited by 4
  11. 2021Fair Comparison: Quantifying Variance in Resultsfor Fine-grained Visual Categorizationcited by 4
  12. 2024Gender Bias in Machine Translation and The Era of Large Language Modelscited by 4
  13. 2021BitextEdit: Automatic Bitext Editing for Improved Low-Resource Machine Translationcited by 3
  14. 2024Machine Translation Models are Zero-Shot Detectors of Translation Directioncited by 3
  15. 2024CKnowEdit: A New Chinese Knowledge Editing Dataset for Linguistics, Facts, and Logic Error Correction in LLMscited by 3
  16. 2025Decoding Machine Translationese in English-Chinese News: LLMs vs. NMTscited by 2
  17. 2025NeoBabel: A Multilingual Open Tower for Visual Generationcited by 2
  18. 2024Enhancing Effectiveness and Robustness in a Low-Resource Regime via Decision-Boundary-aware Data Augmentationcited by 1
  19. 2024The Comparison of Translationese in Machine Translation and Human Transation in terms of Translation Relationscited by 1
  20. 2024Multi-perspective Alignment for Increasing Naturalness in Neural Machine Translationcited by 1
  21. 2025Comparing Native and Non-native English Speakers' Behaviors in Collaborative Writing through Visual Analyticscited by 1
  22. 2025Sociotechnical Effects of Machine Translationcited by 1
  23. 2025Bias Beyond English: Evaluating Social Bias and Debiasing Methods in a Low-Resource Settingcited by 1
  24. 2025Losing our Tail, Again: (Un)Natural Selection & Multilingual LLMscited by 1
  25. 2026Improving Term Evaluation in Machine Translation: Variation Matterscited by 1
  26. 2021Repairing Pronouns in Translation with BERT-Based Post-Editingcited by 0
  27. 2022Comparing Formulaic Language in Human and Machine Translation: Insight from a Parliamentary Corpuscited by 0
  28. 2024Thesis proposal: Are We Losing Textual Diversity to Natural Language Processing?cited by 0
  29. 2025Automatic Machine Translation Detection Using a Surrogate Multilingual Translation Modelcited by 0
  30. 2025Fluent Alignment with Disfluent Judges: Post-training for Lower-resource Languagescited by 0
  31. 2026ConGA: Guidelines for Contextual Gender Annotation. A Framework for Annotating Gender in Machine Translationcited by 0
  32. 2026Is She Even Relevant? When BERT Ignores Explicit Gender Cuescited by 0
  33. 2026Emotion Profiling in LLM-Based Literary Translation: Systematic Shifts Across MT and Post-Editingcited by 0